In recent years, deep learning systems have shown a concerning trend toward increased complexity and higher energy consumption. As researchers in this domain and organizers of one of the Detection and Classification of Acoustic Scenes and Events challenges tasks, we recognize the importance of addressing the environmental impact of data-driven SED systems. In this paper, we propose an analysis focused on SED systems based on the challenge submissions. This includes a comparison across the past two years and a detailed analysis of this year's SED systems. Through this research, we aim to explore how the SED systems are evolving every year in relation to their energy efficiency implications.

翻译：近年来，深度学习系统呈现出日益复杂化和高能耗的趋势。作为该领域的研究人员及声场景与事件检测与分类挑战赛任务的组织者，我们认识到解决数据驱动型声音事件检测系统环境影响的重要性。本文基于挑战赛提交成果，提出针对声音事件检测系统的分析方案，包括过去两年系统的对比分析，以及本年度声音事件检测系统的详细研究。通过本研究，我们旨在探讨声音事件检测系统在能效影响维度上每年的演变规律。